Comprehensive Course Structure
The curriculum of the Bachelor of Computer Science program at Mittal Institute of Technology is designed to provide a robust foundation in computer science principles while offering flexibility for specialization. The structure spans 8 semesters, with each semester consisting of core courses, departmental electives, science electives, and laboratory components.
| Year | Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|---|
| I | I | CS101 | Introduction to Programming | 2-0-2-3 | None |
| II | CS102 | Discrete Mathematics | 3-0-0-3 | None | |
| III | CS103 | Computer Organization | 3-0-0-3 | CS101 | |
| II | IV | CS201 | Data Structures and Algorithms | 3-0-0-3 | CS101 |
| V | CS202 | Database Systems | 3-0-0-3 | CS103 | |
| VI | CS203 | Operating Systems | 3-0-0-3 | CS103 | |
| III | VII | CS301 | Machine Learning | 3-0-0-3 | CS201, CS202 |
| VIII | CS302 | Cybersecurity | 3-0-0-3 | CS203 | |
| IX | CS303 | Software Engineering | 3-0-0-3 | CS201, CS202 | |
| IV | X | CS401 | Final Year Project/Thesis | 4-0-0-4 | CS301, CS302, CS303 |
| XI | CS402 | Internship | 0-0-0-4 | CS301, CS302, CS303 | |
| XII | CS403 | Capstone Design | 4-0-0-4 | CS301, CS302, CS303 |
Advanced Departmental Electives
The advanced departmental electives offered in the program are carefully selected to reflect current industry trends and research directions. Here are descriptions of some key courses:
- Machine Learning and Deep Learning (CS301): This course delves into neural networks, supervised and unsupervised learning, reinforcement learning, and deep learning architectures such as CNNs, RNNs, and Transformers. Students will implement models using TensorFlow and PyTorch.
- Cybersecurity Fundamentals (CS302): The course covers cryptographic algorithms, network security protocols, ethical hacking techniques, and digital forensics. Students will engage in hands-on labs to simulate real-world attack scenarios.
- Software Engineering Principles (CS303): This course explores software development lifecycle, agile methodologies, system design principles, and quality assurance practices. Students will work on team projects using DevOps tools like Jenkins and Docker.
- Data Mining and Analytics (CS304): Students learn data preprocessing techniques, clustering algorithms, classification models, and association rule mining. The course includes practical sessions with tools like Python's Scikit-learn and Apache Spark.
- Human-Computer Interaction (CS305): This course studies cognitive psychology, usability testing methods, and interface design principles. Students will prototype interfaces using Figma and conduct user research.
- Mobile App Development (CS306): The course teaches students how to build cross-platform mobile applications using React Native or Flutter. Emphasis is placed on app store deployment and performance optimization.
- Cloud Computing and DevOps (CS307): Students learn cloud platforms like AWS, Azure, and GCP, along with containerization technologies such as Docker and Kubernetes. Projects involve deploying scalable applications using CI/CD pipelines.
- Game Development (CS308): This course introduces students to game engines like Unity or Unreal Engine, 3D modeling, physics simulation, and scripting languages used in game development.
- Quantum Computing (CS309): An advanced elective focusing on quantum algorithms, quantum circuits, error correction codes, and current research in quantum computing. Students will use IBM Qiskit to run experiments on quantum hardware.
- Computer Vision and Image Processing (CS310): The course covers image enhancement, segmentation techniques, object detection, and facial recognition systems using OpenCV and TensorFlow.
Project-Based Learning Philosophy
The department strongly believes in project-based learning as a means of fostering innovation, teamwork, and practical application of knowledge. Mini-projects are assigned throughout the program to reinforce concepts learned in lectures and labs.
Mini-projects typically span two months and involve small teams of 3-4 students working under faculty guidance. These projects allow students to apply theoretical knowledge to solve real-world problems. Examples include building a chatbot using NLP techniques, designing a smart parking system using sensors and IoT devices, or creating an interactive web application for educational purposes.
The final-year thesis/capstone project is a major component of the program, lasting six months. Students select a topic related to their area of interest and work closely with a faculty advisor. The process involves literature review, problem formulation, methodology development, implementation, testing, and documentation. Final presentations are held in front of a panel of experts.
Project selection is based on student preferences, faculty availability, and research opportunities within the department. Students can propose topics or choose from a list of pre-approved projects. Faculty mentors are assigned based on expertise alignment and project requirements.